My primary goal was to research popular movies vocabulary – words all those heroes actually saying to us and put well known movies onto the scale from the ones with the widest lexicon down to below average range despite possible crowning by IMDB rating, Box Office revenue or number / lack of Academy Awards. This post is the 1st Part of the Movies text analysis and covers Text loading and cleaning using readtext and textclean packages. My first challenge was texts source – I saw text mining projects based on open data scripts but I wanted something new, more direct and, as close to what we actually hear AND read from the screen. I decided directly save subtitles while streaming using Chrome developer panel.
It requires further transformation through the chain .xml > .srt > .txt (not covered here).
Since we have .txt files ready, let`s start from downloading of texts stored in separate folder (1movie = 1 file).
library(readtext)
text_raw <-readtext(paste0(path="C:/Users/Andrew/Documents/R/movies/txt/*"))
text <- gsub("\r?\n|\r", " ", text_raw)
To go further it is important to understand what is text and what is noise here. Since subtitles are real text representation of entire movie, its text part contains:
- Speech (actual text)
- Any sound (made by heroes/characters or around)
- Speakers / characters marks (who is saying)
- Intro / end themes
- Other text
library (textclean) text1 <- str_replace_all (text, "\\[.*?\\]", "") text1_nchar <- sum (nchar (text1))It is important to check how hard your trim your "garden", you could do it element-wise if the summary shows any odd losses or, in opposite, very little decrease of characters #.
the same with () +()
text2 <- str_replace_all(text1, "\\(.*?\\)", "") text2_nchar <- sum (nchar (text2))Do not forget any odd characters detected, like, in my case, ‘File:’
text3 <- str_replace (text2,'File:', '') text3_nchar <- sum (nchar (text3))Now is the trickiest part, other brackets - ♪♪ Ideally, it requires you to know the text well to distinct theme songs and background music from the situation where singing is the way the heroes “talk” to us. For instance musicals or a lot of kids movies.
text4 <- str_replace_all(text3,"\\âTª.*?\\âTª", "") text4_nchar <- sum (nchar (text4))Speech markers e.g. JOHN:
text5 <- str_replace_all(text4,"[a-zA-Z]+:", "") text5_nchar <- sum (nchar (text5))Check % of total cut
Stop cut the weed, let`s replace the rest): @#$%&
text6 <- replace_symbol(text5, dollar = TRUE, percent = TRUE, pound = TRUE,
at = TRUE, and = TRUE, with = TRUE)
text6_nchar <- sum (nchar (text6))
#time stamps
text7 <- replace_time(text6, pattern = "(2[0-3]|[01]?[0-9]):([0-5][0-9])[.:]?([0-5]?[0-9])?")
text7_nchar <- sum (nchar (text7))
#date to text
replace_date_pattern <- paste0(
'([01]?[0-9])[/-]([0-2]?[0-9]|3[01])[/-]\\d{4}|\\d{4}[/-]',
'([01]?[0-9])[/-]([0-2]?[0-9]|3[01])')
text8 <- replace_time(text7, pattern = replace_date_pattern)
text8_nchar <- sum (nchar (text8))
#numbers to text, e.g. 8 = eight
text9 <- replace_number(text8, num.paste = FALSE, remove = FALSE)
text9_nchar <- sum (nchar (text9))
Now texts are clean enough and ready for further analysis. That`s it for Movies text analysis. Part 1.In Movies text analysis. Part 2. I plan to compare how wide, in general, vocabulary is within iconic movies, different genres, Movies vs TV Shows, IMDB TOP 100 vs Box Office All Time Best etc.
In addition, having text data and the movies text length I Planned to check #of words per minute parameter to defy or confirm hypothesis that movies speech is significantly different in range and slower overall than normal speech benchmark in 120-150 WPM (words per minute).
